{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个实例中，我们使用tensorflow来实现一个简单的手写数字识别的网络，并用这个网络来做个简单的识别示例。\n",
    "\n",
    "首先导入一些用到的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "tf.logging.set_verbosity(tf.logging.INFO)  # 这里是设置log里面的警告等级"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先看看数据长什么样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-2-c23ac4ce0bb9>:2: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From D:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "WARNING:tensorflow:From D:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting ./train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From D:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting ./train-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From D:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.one_hot on tensors.\n",
      "Extracting ./t10k-images-idx3-ubyte.gz\n",
      "Extracting ./t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From D:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "(55000, 784)\n",
      "(55000, 10)\n",
      "(5000, 784)\n",
      "(5000, 10)\n",
      "(10000, 784)\n",
      "(10000, 10)\n"
     ]
    }
   ],
   "source": [
    "# 导入数据集\n",
    "mnist = input_data.read_data_sets(\"./\", one_hot=True)  # 对目标值进行one_hot独热编码\n",
    "\n",
    "# 测试机数据形状\n",
    "print(mnist.train.images.shape)\n",
    "print(mnist.train.labels.shape)\n",
    "# 验证集数据情况\n",
    "print(mnist.validation.images.shape)\n",
    "print(mnist.validation.labels.shape)\n",
    "# 测试集数据情况\n",
    "print(mnist.test.images.shape)\n",
    "print(mnist.test.labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到images里面有数量不等的图片，每张图片是28×28长度的一个一维向量，所以用的时候需要先给它还原成28×28的二维图片。\n",
    "\n",
    "labels中则是图片对应的数字的值，这里已经进行了独热编码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "for idx in range(16):\n",
    "    plt.subplot(4,4,idx+1)\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(\"[{}]\".format(np.argmax(mnist.train.labels[idx])))  # argmax()返回最大索引值\n",
    "    plt.imshow(mnist.train.images[idx].reshape(28,28))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来，定义用于训练的网络，首先定义网络的输入。\n",
    "\n",
    "这里我们直接使用上面的数据作为输入，所以定义两个placeholder分别用于图像和label数据，另外，定义一个bool类型的变量用于标识当前网络是否正在训练。\n",
    "\n",
    "为了让网络更高效的运行，多个数据会被组织成一个batch送入网络，两个placeholder的第一维度就是batchsize，因为我们这里还没有确定batchsize的大小，所以第一维度留空None。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, [None,784], name=\"x\")  # 28×28=784\n",
    "y = tf.placeholder(tf.float32, [None,10], name=\"y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为我们输入的是图片展开后的一维向量，所以第一步需要先把一维向量还原成二维的图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_image = tf.reshape(x, [-1, 28, 28, 1])  # 这里的-1不能用None代替，-1表示未知具体个数，到时候自动赋值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来，我们定义第一个卷积层，使用6个5×5的卷积核对输入数据进行卷积（这里的6，可以看作为6个人对数据进行观察，他们每个人的卷积核的参数都不一样），padding方式选择valid（即不用0去补齐，而same则会用0补齐，使得卷积后的大小不变）,所以输出数据的宽高由28×28 --> 24×24，但是深度已经由原来的1层变成6层（因为6个人，每个人都会有一个观察结果）。本层卷积的激活函数为relu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.name_scope(\"conv1\"):\n",
    "    C1 = tf.contrib.slim.conv2d(\n",
    "        x_image,  # 输入的数据\n",
    "        6,  # 6个卷积核，也就是6个人观察\n",
    "        [5,5],  # 卷积核大小\n",
    "        padding=\"VALID\",  # 可选：VALID   SAME\n",
    "        activation_fn=tf.nn.relu  # 激活方式选择relu\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来进行stride为2的最大池化，池化后，输出深度不变，但是输出的宽高由24×24 --> 12×12,深度为6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.name_scope(\"pool1\"):\n",
    "    S2 = tf.contrib.slim.max_pool2d(\n",
    "        C1,  # 输入数据\n",
    "        [2, 2],  # 卷积核大小\n",
    "        stride=[2, 2],  # 移动的步长\n",
    "        padding=\"VALID\",  #  选择补齐方式\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来，我们定义第二个卷积层，使用16个5×5的卷积核对输入数据进行卷积，padding方式还是选择valid，输出由12×12 --> 8×8，深度由6 --> 16 （原因是16个人观察，每个人观察6幅，然后把6个相加成一个，也就是16个人每个人得出一个结果），本层卷积的激活函数为relu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.name_scope(\"conv2\"):\n",
    "    C3 = tf.contrib.slim.conv2d(\n",
    "        S2,\n",
    "        16,\n",
    "        [5,5],\n",
    "        padding=\"VALID\",\n",
    "        activation_fn=tf.nn.relu\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再进行一次stride为2的最大池化，输出由8×8 --> 4×4，池化深度不变，也不用激活函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.name_scope(\"pool2\"):\n",
    "    S4 = tf.contrib.slim.max_pool2d(\n",
    "        C3,\n",
    "        [2,2],\n",
    "        stride=[2, 2],\n",
    "        padding=\"VALID\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "池化后的数据为3维的，（4，4，16），做全连接的时候，我们需要的是一维数据，所以需要将三维转化成一维，我们设置两层全连接的隐层中神经元个数分别120，84"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\layers\\python\\layers\\layers.py:1634: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.flatten instead.\n"
     ]
    }
   ],
   "source": [
    "with tf.name_scope(\"fc1\"):\n",
    "    S4_flat = tf.contrib.slim.flatten(S4)\n",
    "    C5 = tf.contrib.slim.fully_connected(\n",
    "        S4_flat,  # 输入数据（一维）\n",
    "        120,  # 120个神经元\n",
    "        activation_fn=tf.nn.relu  # 激活函数选择relu\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.name_scope(\"fc2\"):\n",
    "    C6 = tf.contrib.slim.fully_connected(\n",
    "        C5,  # 输入数据（一维）\n",
    "        84,  # 84个神经元\n",
    "        activation_fn=tf.nn.relu  # 激活函数选择relu\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对特征添加一个0.6的dropout，以40%的概率丢弃特征中的某些数据\n",
    "\n",
    "这样可以提高网络的推广能力，减少过拟合的可能性。\n",
    "\n",
    "需要注意的是，dropout仅在训练的时候使用，验证的时候，需要关闭dropout，所以验证时候的keep_prob是1.0\n",
    "\n",
    "dropout的输出最终送入一个隐层为10的全连接层，这个全连接层即为最后的分类器。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-12-97da47daac7d>:5: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "with tf.name_scope(\"dropout\"):\n",
    "    keep_prob = tf.placeholder(tf.float32, name=\"keep_prob\")\n",
    "    C6_drop = tf.nn.dropout(\n",
    "        C6,\n",
    "        keep_prob\n",
    "    )\n",
    "    \n",
    "with tf.name_scope(\"fc3\"):\n",
    "    logits = tf.contrib.slim.fully_connected(\n",
    "        C6_drop,\n",
    "        10,\n",
    "        activation_fn=None  # 最后一层不需要激活函数，之后会用softmax处理，得出每种可能的概率\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来，定义loss和用于优化网络的优化器。\n",
    "\n",
    "loss计算使用sparse_softmax_cross_entropy_with_logits，这样做的好处是labels可以不用手动做one_hot省了一些麻烦。\n",
    "\n",
    "这里使用了sgd优化器，学习率为0.3   \n",
    "#### 试试看，增大减小学习率，换个优化器再训练会发生什么。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-13-b174ac857ad0>:2: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cross_entripy_loss = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y)\n",
    ")"
   ]
  },
  {
   "attachments": {
    "%E5%9B%BE%E7%89%87.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![%E5%9B%BE%E7%89%87.png](attachment:%E5%9B%BE%E7%89%87.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tf.add_n( [ list ] ): 求list中元素的累加和，列表里的元素可以是向量，矩阵等\n",
    "l2_loss = tf.add_n(\n",
    "    [\n",
    "        tf.nn.l2_loss(w)\n",
    "        # tf.get_collection('losses'):获取集合“losses”中的所有元素，生成一个列表并返回该列表\n",
    "        for w in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "attachments": {
    "%E5%9B%BE%E7%89%87.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![%E5%9B%BE%E7%89%87.png](attachment:%E5%9B%BE%E7%89%87.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conv/weights:0\n",
      "INFO:tensorflow:Summary name Conv/weights:0 is illegal; using Conv/weights_0 instead.\n",
      "Conv/biases:0\n",
      "INFO:tensorflow:Summary name Conv/biases:0 is illegal; using Conv/biases_0 instead.\n",
      "Conv_1/weights:0\n",
      "INFO:tensorflow:Summary name Conv_1/weights:0 is illegal; using Conv_1/weights_0 instead.\n",
      "Conv_1/biases:0\n",
      "INFO:tensorflow:Summary name Conv_1/biases:0 is illegal; using Conv_1/biases_0 instead.\n",
      "fully_connected/weights:0\n",
      "INFO:tensorflow:Summary name fully_connected/weights:0 is illegal; using fully_connected/weights_0 instead.\n",
      "fully_connected/biases:0\n",
      "INFO:tensorflow:Summary name fully_connected/biases:0 is illegal; using fully_connected/biases_0 instead.\n",
      "fully_connected_1/weights:0\n",
      "INFO:tensorflow:Summary name fully_connected_1/weights:0 is illegal; using fully_connected_1/weights_0 instead.\n",
      "fully_connected_1/biases:0\n",
      "INFO:tensorflow:Summary name fully_connected_1/biases:0 is illegal; using fully_connected_1/biases_0 instead.\n",
      "fully_connected_2/weights:0\n",
      "INFO:tensorflow:Summary name fully_connected_2/weights:0 is illegal; using fully_connected_2/weights_0 instead.\n",
      "fully_connected_2/biases:0\n",
      "INFO:tensorflow:Summary name fully_connected_2/biases:0 is illegal; using fully_connected_2/biases_0 instead.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'total_loss:0' shape=() dtype=string>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for w in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):\n",
    "    print(w.name)\n",
    "    tf.summary.histogram(w.name, w)\n",
    "    \"\"\"\n",
    "    tf.summary.histogram（）将输入的一个任意大小和形状的张量压缩成一个由宽度和数量组成的直方图数据结构．假设输入 [0.5, 1.1, 1.3, 2.2, 2.9, 2.99]，则可以创建三个bin，分别包含0-1之间／1-2之间／2-3之间的所有元素，即三个bin中的元素分别为［0.5］／［1.1，1.3］／［2.2，2.9，2.99］．\n",
    "这样，通过可视化张量在不同时间点的直方图来显示某些分布随时间变化的情况．\n",
    " ———————————————— \n",
    "版权声明：本文为CSDN博主「阿卡蒂奥」的原创文章，遵循CC 4.0 by-sa版权协议，转载请附上原文出处链接及本声明。\n",
    "原文链接：https://blog.csdn.net/akadiao/article/details/79551180\n",
    "    \"\"\"\n",
    "\n",
    "total_loss = cross_entripy_loss + 7e-5 * l2_loss\n",
    "\n",
    "tf.summary.scalar('cross_entropy_loss', cross_entripy_loss)\n",
    "tf.summary.scalar('l2_loss', l2_loss)\n",
    "tf.summary.scalar('total_loss', total_loss)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"\"\"\n",
    "tf.summary.scalar\n",
    "\n",
    "用来显示标量信息，其格式为：\n",
    "\n",
    "                    tf.summary.scalar(tags, values, collections=None, name=None)\n",
    "\n",
    "例如：tf.summary.scalar('mean', mean)\n",
    "\n",
    "一般在画loss,accuary时会用到这个函数。\n",
    " ———————————————— \n",
    "版权声明：本文为CSDN博主「alanjia163」的原创文章，遵循CC 4.0 by-sa版权协议，转载请附上原文出处链接及本声明。\n",
    "原文链接：https://blog.csdn.net/qq_35290785/article/details/89447876\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.3).minimize(total_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "需要注意的是，我们在最后一层全连接输出的logits，是未经softmax的，故不是概率分布，要想看到概率分布，还需对logits做softmax\n",
    "\n",
    "将输出的结果与正确结果（这里指，将softmax中得出的各种分类的概率最大值，也就是预测的最有可能的结果，与真实结果groudtrue进行比较），即可得到我们的网络输出结果的准确率.\n",
    "\n",
    "纠正一下，因为输出的logits的各个分类的值的大小关系，并不会因为softmax而改变，所以直接用logits中最大值的索引与正确结果的one_hot中1的索引进行比较就行了。用softmax只是为了看预测的概率分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = tf.nn.softmax(logits)\n",
    "\n",
    "correct_pred = tf.equal(tf.argmax(y, 1), tf.argmax(logits, 1))\n",
    "# 在矩阵的结构中，axis 可被设置为 0 或 1，分别表示 0：按列计算，1：按行计算。\n",
    "# axis=0 时，比较每一列的元素，将每一列最大元素所在的索引记录下来，最后返回每一列最大元素所在的索引数组。\n",
    "# axis=1 时，比较每一行的元素，将每一行最大元素所在的索引记录下来，最后返回每一行最大元素所在的索引数组。\n",
    "# 0 表示的是按列比较返回最大值的索引，1 表示按行比较返回最大值的索引。\n",
    "\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))  # tf.cast()作用是转换类型，比如这里将correct_pred的类型转换成float32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "saver用来保存或恢复训练的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "traning_step = 1100  # 100*1100的话就可以将训练数据全部训练两遍ephco=2\n",
    "\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上定义的所有操作，均为计算图，也就是仅仅是定义了网络的结构，实际需要运行的话，还需要创建一个session，并将数据填入网络中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 100 training steps, the loss is 0.787482, the validation accuracy is 0.8966\n",
      "after 200 training steps, the loss is 0.431711, the validation accuracy is 0.9236\n",
      "after 300 training steps, the loss is 0.278438, the validation accuracy is 0.963\n",
      "after 400 training steps, the loss is 0.120657, the validation accuracy is 0.968\n",
      "after 500 training steps, the loss is 0.211906, the validation accuracy is 0.9672\n",
      "after 600 training steps, the loss is 0.0801194, the validation accuracy is 0.9776\n",
      "after 700 training steps, the loss is 0.0246748, the validation accuracy is 0.9782\n",
      "after 800 training steps, the loss is 0.00893961, the validation accuracy is 0.9792\n",
      "after 900 training steps, the loss is 0.0458274, the validation accuracy is 0.9802\n",
      "after 1000 training steps, the loss is 0.133972, the validation accuracy is 0.9804\n",
      "the training is finish!\n",
      "the test accuracy is : 0.978\n"
     ]
    }
   ],
   "source": [
    "merged = tf.summary.merge_all()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    writer = tf.summary.FileWriter(\"./logs/\",sess.graph)\n",
    "    \n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    # 定义验证集和测试集\n",
    "    validation_data = {\n",
    "        x: mnist.validation.images,\n",
    "        y: mnist.validation.labels,\n",
    "        keep_prob:1.0\n",
    "    }\n",
    "    test_data = {\n",
    "        x: mnist.test.images,\n",
    "        y: mnist.test.labels,\n",
    "        keep_prob:1.0\n",
    "    }\n",
    "    \n",
    "    for i in range(traning_step):\n",
    "        xs, ys = mnist.train.next_batch(batch_size)\n",
    "        _, loss, rs = sess.run(\n",
    "            [optimizer, cross_entripy_loss, merged],\n",
    "            feed_dict={\n",
    "                x:xs,\n",
    "                y:ys,\n",
    "                keep_prob:0.6\n",
    "            }\n",
    "        )\n",
    "        writer.add_summary(rs, i)\n",
    "        \n",
    "        # 每100次训练打印一次损失值与验证准确率\n",
    "        if i>0 and i % 100 == 0:\n",
    "            validate_accuracy = sess.run(accuracy, feed_dict=validation_data)\n",
    "            print(\n",
    "                \"after %d training steps, the loss is %g, the validation accuracy is %g\" % (i, loss, validate_accuracy)\n",
    "            )\n",
    "            saver.save(sess, \"./model.ckpt\",global_step=i)\n",
    "    print(\"the training is finish!\")\n",
    "    # 最终的测试准确率\n",
    "    acc = sess.run(accuracy, feed_dict=test_data)\n",
    "    print(\"the test accuracy is :\", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model.ckpt-1000\n",
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "with tf.Session() as sess:\n",
    "    ckpt = tf.train.get_checkpoint_state('./')\n",
    "    if ckpt and ckpt.model_checkpoint_path:\n",
    "        saver.restore(sess, ckpt.model_checkpoint_path)\n",
    "        final_pred, acc =sess.run(\n",
    "        [pred, accuracy],\n",
    "        feed_dict={\n",
    "            x: mnist.test.images[:16],  # 前16行数据导入给x\n",
    "            y: mnist.test.labels[:16],\n",
    "            keep_prob:1.0\n",
    "        }) \n",
    "    orders = np.argsort(final_pred)  # 返回的是final_pred的大小排列的索引值的列表,这里的final_pred是softmax后的概率分布\n",
    "    plt.figure(figsize = (8, 8))\n",
    "    print(acc)\n",
    "    for idx in range(16):\n",
    "        order = orders[idx, :][-1] # 表示取第idx行数据的最后一个值，也就是概率最大的值的索引值\n",
    "        prob = final_pred[idx, :][order]  # 对应的就是取到了这行数据中概率最大的概率值\n",
    "        plt.subplot(4, 4, idx+1)\n",
    "        plt.axis(\"off\")\n",
    "        plt.title('{}: [{}]-[{:.1f}%]'.format(np.argmax(mnist.test.labels[idx]),order, prob*100))\n",
    "        plt.imshow(mnist.test.images[idx].reshape(28,28))\n",
    "        \n",
    "    else:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 卷积特性描述"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 工作原理\n",
    "   \n",
    "   用一个小的权重矩阵去覆盖输入数据，对应位置元素加权相乘，求和后的结果作为该卷积核中心点对应的像素卷积操作后的像素值，这个带有权重的卷积核在待处理的二维数据上面按照规律滑动，最后形成一个新的矩阵。这里需要注意的是，如果待进行卷积操作的数据有几个深度，一层深度的卷积核在对每个深度计算完后进行加处理，最后不管待处理的深度多少，出来的结果深度都为1.也就是说，如果卷积核的深度为n，卷积后的深度也是n，不管被卷积的数据的深度为多少。\n",
    "    \n",
    "   2. 局部相关性\n",
    "   \n",
    "   *相邻的像素结合起来表达一个特征，距离较远的像素影响较小；\n",
    "   \n",
    "   *随着层数的增加，feature map里面的点 映射到原图的感受野越来越大"
   ]
  },
  {
   "attachments": {
    "%E5%9B%BE%E7%89%87.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![%E5%9B%BE%E7%89%87.png](attachment:%E5%9B%BE%E7%89%87.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    *不仅仅可以适用于图像，其他具有局部相关性的数据均可以尝试卷积网络处理\n",
    "    \n",
    "    *带来一定的遮挡不变性\n",
    "   \n",
    "    *参数共享\n",
    "    \n",
    "    *平移宽容\n",
    "    \n",
    "    *在任何位置都可以激活神经元\n",
    "    \n",
    "    *缩放宽容\n",
    "    \n",
    "    *在一定程度内的图像缩放（通常认为25%以内）不会产生太大干扰\n",
    "    \n",
    "    *少许降维：\n",
    "    \n",
    "    例如：10×10的图像，用5×5的卷积核，采用\"VALID\"方式（padding=0），stride=2，那么输出的feature map大小为： （10-5 + 0*2）/2 + 1 = 3 ，3×3\n",
    "    \n",
    "    *对输入的尺寸不做要求\n",
    "    \n",
    "    *全连接里面，权重W的尺寸是由输入决定的\n",
    "    \n",
    "    *卷积核的尺寸只与网络设计有关，与输入没有任何关系\n",
    "    \n",
    "    *卷积核固定的情况下，feature的尺寸是随输入变化的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 为什么卷积的效果要比全连接网络好"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 卷积操作可以自己设计特征检测算子\n",
    "   \n",
    "   2. 经过多层提取，可以看到整体\n",
    "   \n",
    "   3. 卷积操作考虑了数据间的局部相关性，空间相关性"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
